The Tello sdk has the streamon and streamoff commands. These start and end the 720p video stream. Is there a way to get a 5 megapixel image through the sdk, like when you put the control app into photo mode? I'm guessing if such a way exists, it is not documented.
while there isn't a UDP command that you can send to the drone to switch, there is some python code that you could try in order to make it work. and to make something like that work easily without coding it over again putting it in a function and calling it like "takePhoto()" is the best solution I have. Here's some code:
# # simple ex_4: Tello_photo
# <\>
from djitellopy import tello
import cv2
me = tello.Tello()
me.connect()
print(me.get_battery())
def TakePhoto(me):
me.streamoff()
me.streamon()
img = me.get_frame_read().frame
# print(img)
# img = cv2.resize(img, (360, 240)) # optional
cv2.imshow("photo", img) # optional display photo
cv2.waitKey(0)
# # example condition, it can also just be called normally like "TakePhoto"
"""
if (1 + 1) == 2:
TakePhoto(me)
"""
# # another example, with an actual used if statement
"""
bat = me.get_battery()
if bat < 50:
TakePhoto(me)
else:
print("battery too low (for ex)")
"""
# # for now, we'll call it just like this for testing:
TakePhoto(me)
# </>
the code was tested on windows 10 - pycharm 2021.2 > python3.x >> opencv 4.5.1.48
I hope this helped and I hope it works for you.
Related
We are developing a speech application using Google's speech-to-text API. Now our data (audio files) get stored in S3 bucket on AWS. is there a way to directly pass the S3 URI to Google's speech-to-text API?
From their documentation it seems this is at the moment not possible in Google's speech-to-text API
This is not the case for their vision and NLP APIs.
Any ideas why this limitation for speech APIs?
And whats a good work around for this?
Currently, Google only allows audio files from either your local source or from Google's Cloud Storage. No reasonable explanation is given on the documentation about this.
Passing audio referenced by a URI
More typically, you will pass a uri parameter within the Speech request's audio field, pointing to an audio file (in binary format, not base64) located on Google Cloud Storage
I suggest you move your files to Google Cloud Storage. If you don't want to, there is a good workaround:
Use Google Cloud Speech API with streaming API. You are not required to store anything anywhere. Your speech application provides input from any microphone. And don't worry if you don't know handling inputs from microphone.
Google provides a sample code that does it all:
# [START speech_transcribe_streaming_mic]
from __future__ import division
import re
import sys
from google.cloud import speech
import pyaudio
from six.moves import queue
# Audio recording parameters
RATE = 16000
CHUNK = int(RATE / 10) # 100ms
class MicrophoneStream(object):
"""Opens a recording stream as a generator yielding the audio chunks."""
def __init__(self, rate, chunk):
self._rate = rate
self._chunk = chunk
# Create a thread-safe buffer of audio data
self._buff = queue.Queue()
self.closed = True
def __enter__(self):
self._audio_interface = pyaudio.PyAudio()
self._audio_stream = self._audio_interface.open(
format=pyaudio.paInt16,
channels=1,
rate=self._rate,
input=True,
frames_per_buffer=self._chunk,
# Run the audio stream asynchronously to fill the buffer object.
# This is necessary so that the input device's buffer doesn't
# overflow while the calling thread makes network requests, etc.
stream_callback=self._fill_buffer,
)
self.closed = False
return self
def __exit__(self, type, value, traceback):
self._audio_stream.stop_stream()
self._audio_stream.close()
self.closed = True
# Signal the generator to terminate so that the client's
# streaming_recognize method will not block the process termination.
self._buff.put(None)
self._audio_interface.terminate()
def _fill_buffer(self, in_data, frame_count, time_info, status_flags):
"""Continuously collect data from the audio stream, into the buffer."""
self._buff.put(in_data)
return None, pyaudio.paContinue
def generator(self):
while not self.closed:
# Use a blocking get() to ensure there's at least one chunk of
# data, and stop iteration if the chunk is None, indicating the
# end of the audio stream.
chunk = self._buff.get()
if chunk is None:
return
data = [chunk]
# Now consume whatever other data's still buffered.
while True:
try:
chunk = self._buff.get(block=False)
if chunk is None:
return
data.append(chunk)
except queue.Empty:
break
yield b"".join(data)
def listen_print_loop(responses):
"""Iterates through server responses and prints them.
The responses passed is a generator that will block until a response
is provided by the server.
Each response may contain multiple results, and each result may contain
multiple alternatives; for details, see the documentation. Here we
print only the transcription for the top alternative of the top result.
In this case, responses are provided for interim results as well. If the
response is an interim one, print a line feed at the end of it, to allow
the next result to overwrite it, until the response is a final one. For the
final one, print a newline to preserve the finalized transcription.
"""
num_chars_printed = 0
for response in responses:
if not response.results:
continue
# The `results` list is consecutive. For streaming, we only care about
# the first result being considered, since once it's `is_final`, it
# moves on to considering the next utterance.
result = response.results[0]
if not result.alternatives:
continue
# Display the transcription of the top alternative.
transcript = result.alternatives[0].transcript
# Display interim results, but with a carriage return at the end of the
# line, so subsequent lines will overwrite them.
#
# If the previous result was longer than this one, we need to print
# some extra spaces to overwrite the previous result
overwrite_chars = " " * (num_chars_printed - len(transcript))
if not result.is_final:
sys.stdout.write(transcript + overwrite_chars + "\r")
sys.stdout.flush()
num_chars_printed = len(transcript)
else:
print(transcript + overwrite_chars)
# Exit recognition if any of the transcribed phrases could be
# one of our keywords.
if re.search(r"\b(exit|quit)\b", transcript, re.I):
print("Exiting..")
break
num_chars_printed = 0
def main():
language_code = "en-US" # a BCP-47 language tag
client = speech.SpeechClient()
config = speech.RecognitionConfig(
encoding=speech.RecognitionConfig.AudioEncoding.LINEAR16,
sample_rate_hertz=RATE,
language_code=language_code,
)
streaming_config = speech.StreamingRecognitionConfig(
config=config, interim_results=True
)
with MicrophoneStream(RATE, CHUNK) as stream:
audio_generator = stream.generator()
requests = (
speech.StreamingRecognizeRequest(audio_content=content)
for content in audio_generator
)
responses = client.streaming_recognize(streaming_config, requests)
# Now, put the transcription responses to use.
listen_print_loop(responses)
if __name__ == "__main__":
main()
# [END speech_transcribe_streaming_mic]
Dependencies are google-cloud-speech and pyaudio
For AWS S3, you can store your audio files there before/after you get the transcripts from Google Speech API.
Streaming is super fast as well.
And don't forget to include your credentials. You need to get authorized first by providing GOOGLE_APPLICATION_CREDENTIALS
Python v3.5, Windows 10
I'm using multiple processes and trying to captures user input. Searching everything I see there are odd things that happen when using input() with multiple processes. After 8 hours+ of trying, nothing I implement worked, I'm positive I am doing it wrong but I can't for the life of me figure it out.
The following is a very stripped down program that demonstrates the issue. Now it works fine when I run this program within PyCharm, but when I use pyinstaller to create a single executable it fails. The program constantly is stuck in a loop asking the user to enter something as shown below:.
I am pretty sure it has to do with how Windows takes in standard input from things I've read. I've also tried passing the user input variables as Queue() items to the functions but the same issue. I read you should put input() in the main python process so I did that under if __name__ = '__main__':
from multiprocessing import Process
import time
def func_1(duration_1):
while duration_1 >= 0:
time.sleep(1)
print('Duration_1: %d %s' % (duration_1, 's'))
duration_1 -= 1
def func_2(duration_2):
while duration_2 >= 0:
time.sleep(1)
print('Duration_2: %d %s' % (duration_2, 's'))
duration_2 -= 1
if __name__ == '__main__':
# func_1 user input
while True:
duration_1 = input('Enter a positive integer.')
if duration_1.isdigit():
duration_1 = int(duration_1)
break
else:
print('**Only positive integers accepted**')
continue
# func_2 user input
while True:
duration_2 = input('Enter a positive integer.')
if duration_2.isdigit():
duration_2 = int(duration_2)
break
else:
print('**Only positive integers accepted**')
continue
p1 = Process(target=func_1, args=(duration_1,))
p2 = Process(target=func_2, args=(duration_2,))
p1.start()
p2.start()
p1.join()
p2.join()
You need to use multiprocessing.freeze_support() when you produce a Windows executable with PyInstaller.
Straight out from the docs:
multiprocessing.freeze_support()
Add support for when a program which uses multiprocessing has been frozen to produce a Windows executable. (Has been tested with py2exe, PyInstaller and cx_Freeze.)
One needs to call this function straight after the if name == 'main' line of the main module. For example:
from multiprocessing import Process, freeze_support
def f():
print('hello world!')
if __name__ == '__main__':
freeze_support()
Process(target=f).start()
If the freeze_support() line is omitted then trying to run the frozen executable will raise RuntimeError.
Calling freeze_support() has no effect when invoked on any operating system other than Windows. In addition, if the module is being run normally by the Python interpreter on Windows (the program has not been frozen), then freeze_support() has no effect.
In your example you also have unnecessary code duplication you should tackle.
How to pass commandline argument when running a python code in google colab?
I have written a code which takes a file as input via sys.argv[]. How do I do this?
As far as I know, there is no special way to pass command line arguments to python code. This is a working code sample I use to when creating tfrecords.
!python generate_tfrecord.py --csv_input=data/test_labels.csv --output_path=data/test.record --image_dir=images/
I don't see any difference between the regular command line python argument passing and the colab. Please add more code to your question to get better help.
I tried this in a google colab notebook
import sys
sys.argv[0] = "first_arg" # this is to assign the first command line argument
sys.argv[1] = "second_arg" # This line to assign the second arg for example
And it worked for me.
So if you want to run a python code that works like this:
!python test.py --image_folder '/content/image' --workers 2 --Prediction CTC --rgb True
You have to open test.py or your file with editor then you will find line inside the file similer like this:
parser = argparse.ArgumentParser()
parser.add_argument('--image_folder', required=True, help='path to image_folder')
parser.add_argument('--workers', type=int, default=1, help='number of workers')
parser.add_argument('--Prediction', type=str, default='CTC', help='Prediction stage.')
parser.add_argument('--rgb', action='store_true', help='use rgb input')
args = parser.parse_args()
But this will give you " Error SystemExit: 2 "
Then you have to change like this:
parser = argparse.ArgumentParser()
parser.add_argument('--image_folder', required=False, default='/content/image', help='path to image_folder')
parser.add_argument('--workers', type=int, default=2, help='number of workers')
parser.add_argument('--Prediction', type=str, default='CTC', help='Prediction stage.')
parser.add_argument('--rgb', action='store_false', help='use rgb input')
parser.add_argument("-f", "--file", required=False)
args = parser.parse_args()
You must add in the end of " parser.add_argument " line:
parser.add_argument("-f", "--file", required=False)
Then you can call commandline argument like this:
image = args.image_path
Or
img = Image.open(args.image_path)
workers = args.workers
But if your last line like this:
args = vars(ap.parse_args())
Then you have to call it like this:
image = args["image_path"]
Or
img = Image.open(args["image_path"])
workers = args["workers"]
#Note ( action='store_false' ) will default to ( False )
Likewise, ( action='store_false' ) will default to ( True )
Tested with Google colab
I made a bioinformatic tool locally in my machine to parse Uniprot big data files of proteins.
The tool I made needs the passing of different parameters using command line arguments. After the tool was working locally, I upload data files and python source files to my google drive.
I did not make any changes to my files. I just run directly the following command in google colab:
!python3 drive/MyDrive/uniprot/uniprot_select.py FIELDS "ID,OS,SQ" FROM drive/MyDrive/data/uniprot.dat WHERE "SQ#EYDRRR" FASTA
It works perfectly!
No need of special parsing, no need to additional imports. All the work you normally do locally in your machine, can be executed without changes.
I'm trying to run a test python code to use the traci library and it is returning "please declare environment SUMO_HOME".
I'm on Ubuntu 18.4.2 and Sumo 0.32.0.I solved this problem before by running
export SUMO_HOME=/home/gustavo/Downloads/sumo-0.32.0/tools/
,but this time it couldn't solve the problem. So I tried implementing a line inside the python file using the os library giving the same command but from the code itself:
os.system("export SUMO_HOME=/home/gustavo/Downloads/sumo-0.32.0/tool/")
And it also didn't work, so came here to ask for help. May any of you help me, please?
import os
import sys
import optparse
os.system("export SUMO_HOME=/home/gustavo/Downloads/sumo-0.32.0/tool/")
# we need to import some python modules from the $SUMO_HOME/tools directory
if 'SUMO_HOME' in os.environ:
tools = os.path.join(os.environ['SUMO_HOME=/home/gustavo/Downloads/sumo-0.32.0/tools/'], 'tools')
sys.path.append(tools)
else:
sys.exit("please declare environment variable 'SUMO_HOME'")
from sumolib import checkBinary # Checks for the binary in environ vars
import traci
def get_options():
opt_parser = optparse.OptionParser()
opt_parser.add_option("--nogui", action="store_true",
default=False, help="run the commandline version of sumo")
options, args = opt_parser.parse_args()
return options
# contains TraCI control loop
def run():
step = 0
while traci.simulation.getMinExpectedNumber() > 0:
traci.simulationStep()
print(step)
step += 1
traci.close()
sys.stdout.flush()
# main entry point
if __name__ == "__main__":
options = get_options()
# check binary
if options.nogui:
sumoBinary = checkBinary('sumo')
else:
sumoBinary = checkBinary('sumo-gui')
# traci starts sumo as a subprocess and then this script connects and runs
traci.start([sumoBinary, "-c", "demo.sumocfg",
"--tripinfo-output", "tripinfo.xml"])
run()
I expected for the steps to appear on the terminal.
The correct location is probably
export SUMO_HOME=/home/gustavo/Downloads/sumo-0.32.0
without the tools or tool suffix. It will not work from inside the python script with os.system but you could modify os.environ directly.
Furthermore you mixed up the call to os.environ in the script. It should read:
tools = os.path.join(os.environ['SUMO_HOME'], 'tools')
I swapped the if else part for another code :
try:
sys.path.append("/home/gustavo/Downloads/sumo-0.32.0/tools")
from sumolib import checkBinary
except ImportError:
sys.exit("please declare environment variable 'SUMO_HOME' as the root directory of your sumo installation (it should contain folders 'bin', 'tools' and 'docs')")
It solved the problem
I am looking for a way to print 3D pdf from the results ABAQUS/Viewer. This will make it easy to communicate the results with others who are interested in the results of simulation but do not have access to ABAQUS.
The best way is to export a vrml file and convert it using Tetra4D or pdf3D and Adobe Acrobat professional. The 3D pdfs can look very good. However, the commercial software would cost over £800 per year. I did create a Python script to create a 3D pdf directly from Abaqus/CAE & Viewer which uses 2 open source tools: 1) Meshlab (http://www.meshlab.net/) to create a U3D file, 2) MiKTeX (https://miktex.org/) to convert the U3D file into a pdf. The output is not as polished as Tetra4D but it works. I have not tried this with the latest version of Meshlab. Just run this script from Abaqus/CAE or Abaqus/Viewer.
# Abaqus CAE/Viewer Python Script to create a 3D pdf directly from Abaqus/CAE or Abaqus/Viewer.
# You must first install meshlab (meshlabserver.exe)and MiKTeX (pdflatex.exe)
# Edit this script to reflect the installed locations of meshlabserver.exe and pdflatex.exe
# It will export a stl or obj file the mesh of current viewport and convert into 3D pdf
# Or run in Abaqus/viewer and it will create a VRML file and convert to 3D pdf.
# If contours are displayed in Abaqus Viewer, then it will create a contour 3D pdf
from abaqus import *
from abaqusConstants import *
from viewerModules import *
import os
import subprocess
import sys
# -----------------------------------------------------------------------------
pdfName='try'
meshlab_path="C:/Program Files/VCG/MeshLab/meshlabserver.exe"
pdfLatex_path="C:/Program Files (x86)/MiKTeX 2.9/miktex/bin/pdflatex.exe"
# -----------------------------------------------------------------------------
currView=session.viewports[session.currentViewportName]
try: # for Abaqus Viewer
cOdbD=currView.odbDisplay
odb = session.odbs[cOdbD.name]
name=odb.name.split(r'/')[-1].replace('.odb','')
module='Vis'
except: # Abaqus CAE
#name=currView.displayedObject.modelName
import stlExport_kernel
name = repr(currView.displayedObject).split('[')[-1].split(']')[0][1:-1] # allows for either main or visulation modules
module='CAE'
print module
if module=='CAE':
#All instances must be meshed
cOdbD=None
try:
ext='.stl'
stlExport_kernel.STLExport(moduleName='Assembly', stlFileName=pdfName + ext, stlFileType='BINARY')
except:
try:
ext='.obj'
session.writeOBJFile(fileName=os.path.join(directory,pdfName + ext), canvasObjects= (currView, ))
except:
print 'Either your assembly is not fully meshed or something else'
directory=(os.getcwd())
else: # Abaqus/Viewer
if cOdbD.viewCut:
session.graphicsOptions.setValues(antiAlias=OFF) # Better with anti aliasing off
odb = session.odbs[cOdbD.name]
directory=odb.path.replace(odb.path.split('/')[-1],'').replace('/','\\')
# Turn off most of the stuff in the viewport
currView.viewportAnnotationOptions.setValues(triad=OFF,
legend=OFF, title=OFF, state=OFF, annotations=OFF, compass=OFF)
ext='.wrl'
session.writeVrmlFile(fileName=os.path.join(directory,pdfName + ext),
compression=0, canvasObjects= (currView, ))
pdfFilePath=os.path.join(directory,pdfName+'-out.pdf')
if os.path.isfile(pdfFilePath):
os.remove(pdfFilePath)
#Check file was deleted
if os.path.isfile(pdfFilePath):
print "Aborted because pdf file of same name cant be deleted. Please close programs which it might be open in"
1/0 #a dodgy way to exit program
# Invoke meshlab to convert to a .u3d file
if cOdbD: #If in Abaqus/viewer
if 'CONTOURS' in repr(cOdbD.display.plotState[0]): # If contours are displayed. Output contoured pdf
p=subprocess.Popen([meshlab_path,'-i',pdfName + ext, '-o',pdfName + '.u3d','-m','vc']) #'vn fn fc vt'
else:
p=subprocess.Popen([meshlab_path,'-i',pdfName + ext, '-o',pdfName + '.u3d'])
else:
p=subprocess.Popen([meshlab_path,'-i',pdfName + ext, '-o',pdfName + '.u3d'])
p.communicate() # Wait for meshlab to finish
file_fullPathName=os.path.join(directory, pdfName + '.tex')
#Read the .tex file which meshlab has just created
with open(file_fullPathName, 'r') as texFile:
lines = texFile.read()
#Edit the .tex file
lines=lines.replace("\usepackage[3D]{movie15}","\\usepackage[3D]{movie15}\n\\usepackage[margin=-2.2in]{geometry}")
if cOdbD:
if 'CONTOURS' in repr(cOdbD.display.plotState[0]):
lines=lines.replace("3Dlights=CAD,","3Dlights=CAD,\n\t3Drender=SolidWireframe,")
lines=lines.replace("\n\end{document}","{---------------------------------------------------------------------------------Click above! MB1 - rotate, MB2 wheel or MB3 - zoom, Ctrl-MB1 - pan--------------}\n\\end{document}")
file_fullPathName=os.path.join(directory, pdfName + '-out.tex')
with open(file_fullPathName, "w") as outp:
outp.write(lines)
p=subprocess.Popen([
pdfLatex_path,
pdfName + '-out.tex',
])
p.communicate()
print 'Conversion to pdf complete'
print file_fullPathName
The simplest way of printing the Abaqus *.odb results are using Tecplot 360 which is read the Abaqus *.odb files and you can get the *.tif and *.png results with any resolutions and you can also rotate the model in 3D and change the fonts and all the things you need.